MCP Toolz

mcp-name: io.github.taylorleese/mcp-toolz

Save contexts and todos across Claude Code sessions, get feedback from ChatGPT, Claude, Gemini, and DeepSeek.

Features

🔌 MCP Server: Works NOW with Claude Code - full tool integration ready
Session Continuity: Never lose context when restarting Claude Code - restore "what was I working on last session"
Project Organization: Contexts and todos automatically organized by project directory
Session Tracking: Every Claude Code session gets a unique ID - track your work over time
AI Feedback: Get feedback from ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), and DeepSeek on your code and decisions
Context Types: Save conversations, code snippets, architectural suggestions, or error traces
Persistent Todos: Save and restore your todo list across sessions - never forget where you left off
Full-Text Search: Find anything by content, tags, project, or session
CLI + MCP: Use via Claude Code MCP tools or standalone CLI commands

Quick Start

Installation

From PyPI (Recommended)

pip install mcp-toolz

From Source (Development)

# Clone the repository
git clone https://github.com/taylorleese/mcp-toolz.git
cd mcp-toolz

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # macOS/Linux
# or: venv\Scripts\activate  # Windows

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

Configuration

# Set your API keys as environment variables (at least one required for AI features)
export OPENAI_API_KEY=sk-...           # For ChatGPT
export ANTHROPIC_API_KEY=sk-ant-...    # For Claude
export GOOGLE_API_KEY=...              # For Gemini
export DEEPSEEK_API_KEY=sk-...         # For DeepSeek

# Or create a .env file (if installing from source)
cp .env.example .env
# Edit .env and add your API keys

MCP Server Setup (Recommended)

The primary way to use mcp-toolz is via the MCP server in Claude Code:

Add to Claude Code settings (add this JSON to your Claude Code MCP settings):

If installed via pip:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GOOGLE_API_KEY": "...",
        "DEEPSEEK_API_KEY": "sk-..."
      }
    }
  }
}

If installed from source:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/absolute/path/to/mcp-toolz",
      "env": {
        "PYTHONPATH": "/absolute/path/to/mcp-toolz/src"
      }
    }
  }
}

Configure API keys - Add your API keys to the env section (pip) or .env file (source)
Restart Claude Code to load the MCP server
Use MCP tools in Claude Code:
- "Save this context about authentication"
- "Ask ChatGPT about the last context I saved"
- "Show my active todos"
- "Search contexts tagged with 'bug'"

All MCP tools are automatically available - see MCP Server Tools below.

MCP Server Tools

The MCP server works NOW with Claude Code and provides these tools:

Context Tools:

context_save - Save a new context (automatically includes session info)
context_search - Search by query or tags
context_get - Get by ID
context_list - List recent
context_delete - Delete by ID

AI Feedback Tools:

ask_chatgpt - Get ChatGPT's analysis of a context (supports custom questions)
ask_claude - Get Claude's analysis of a context (supports custom questions)
ask_gemini - Get Gemini's analysis of a context (supports custom questions)
ask_deepseek - Get DeepSeek's analysis of a context (supports custom questions)

Todo Tools:

todo_search - Search snapshots
todo_get - Get by ID
todo_list - List recent
todo_save - Save snapshot
todo_restore - Get active/specific snapshot
todo_delete - Delete by ID

Session Tracking: When saving contexts through MCP tools, they are automatically tagged with:

Current project directory (project_path)
Session ID (unique per Claude Code session)
Session timestamp (when the session started)

Future: Once ChatGPT Desktop adds MCP support, you'll be able to use these same tools there too.

Usage Examples

Here are practical examples of how to use mcp-toolz in Claude Code:

Example 1: Get Multiple AI Perspectives on Architecture Decisions

Prompt:

I'm deciding between using Redis or Memcached for caching user sessions.
Save this as a context and ask ChatGPT for their analysis.
Use tags: caching, redis, memcached, architecture

What happens:

Claude Code uses context_save to save your architectural decision
Then uses ask_chatgpt to get ChatGPT's perspective
You can compare multiple AI perspectives to inform your decision

Follow-up prompts:

"Ask Claude the same question for comparison"
"Ask Gemini for another perspective"
"What does DeepSeek think about this?"
"Search my contexts tagged with 'architecture'"

Example 2: Session Continuity - Never Lose Your Place

Prompt (end of work session):

Save my current todo list so I can restore it tomorrow

What happens:

Claude Code uses todo_save to snapshot your current work state
Todos are saved with project path and timestamp

Next day prompt:

What was I working on yesterday? Restore my todos.

What happens:

Claude Code uses todo_restore to get your last snapshot
Shows you exactly where you left off
You can jump right back into work

Example 3: Debug with Multiple AI Perspectives

Prompt:

I'm getting "TypeError: Cannot read property 'map' of undefined" in my React component.
The error occurs in UserList.jsx when rendering the users array.
Save this as an error context and ask ChatGPT, Claude, and Gemini for debugging suggestions.
Tags: react, debugging, javascript

What happens:

Claude Code uses context_save to record the error
Uses ask_chatgpt to get OpenAI's debugging approach
Uses ask_claude to get Anthropic's perspective
Uses ask_gemini for Google's analysis
You can compare different debugging strategies from multiple AI models

Follow-up prompts:

"Search for other contexts tagged with 'react' bugs"
"Show me contexts from my last session"

Example 4: Track Performance Optimization Ideas

Prompt:

Save this performance optimization idea: "Lazy load images below the fold using
Intersection Observer API. Estimated 40% reduction in initial page load."
Type: suggestion, Tags: performance, optimization, images

What happens:

Claude Code uses context_save with type "suggestion"
Context is searchable and tied to current project
Available across all future sessions

Later prompt:

Search my contexts for performance optimization ideas

What happens:

Claude Code uses context_search with your query
Returns all matching contexts across sessions
You can review past optimization ideas

Prompt (in Project A):

I figured out how to handle OAuth refresh tokens properly.
Save this so I can reference it in other projects:
"Store refresh tokens in httpOnly cookies, access tokens in memory only.
Rotate refresh tokens on each use. Set 7-day expiry on refresh, 15min on access."
Type: code, Tags: oauth, security, authentication

Prompt (later in Project B):

How did I implement OAuth refresh tokens in my last project?
Search for contexts about oauth and show me what I saved.

What happens:

Claude Code uses context_search to find your OAuth implementation
Retrieves the context across projects
You reuse your own knowledge without starting from scratch

mcp-toolz makes it easy to share contexts and todos across multiple Claude Code sessions or agents.

MCP Resources (Passive Discovery)

Claude Code can automatically discover and read contexts/todos via MCP resources:

Context Resources:

mcp-toolz://contexts/project/recent - Recent contexts for current project
mcp-toolz://contexts/project/sessions - List of recent Claude Code sessions for current project
mcp-toolz://contexts/session/{session_id} - All contexts from a specific session

Todo Resources:

mcp-toolz://todos/recent - Last 20 todo snapshots (all projects)
mcp-toolz://todos/active - Active todos for current working directory

Session Tracking: Each Claude Code session automatically gets a unique session ID. All contexts saved during that session are tagged with:

session_id - UUID of the Claude Code session
session_timestamp - When the session started
project_path - Directory where the context was created

This makes it easy to restore context from previous sessions: "Show me what I was working on in my last session"

Resources are read-only views into the shared database. Claude Code can discover them automatically without explicit tool calls.

Shared Database Setup

By default, mcp-toolz stores all data in ~/.mcp-toolz/contexts.db, which is automatically shared across all projects on the same machine. No additional configuration needed!

For advanced use cases (syncing across multiple machines via Dropbox, iCloud, etc.):

Choose a synced location for the database:

# Example: Use a synced folder (Dropbox, iCloud, network drive)
mkdir -p ~/Dropbox/mcp-toolz-shared

Update .env file or MCP config to point to the synced database:

# In .env file
MCP_TOOLZ_DB_PATH=~/Dropbox/mcp-toolz-shared/contexts.db

Or in your MCP config:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/absolute/path/to/mcp-toolz",
      "env": {
        "PYTHONPATH": "/absolute/path/to/mcp-toolz/src",
        "MCP_TOOLZ_DB_PATH": "/Users/you/Dropbox/mcp-toolz-shared/contexts.db"
      }
    }
  }
}

Restart Claude Code - it now uses the synced database location

How It Works

Contexts: Organized by project_path (each directory gets its own contexts)
Session Tracking: Contexts tagged with session ID and timestamp for easy restoration
Todos: Organized by project_path (each directory gets its own snapshots)
Single SQLite DB: All data stored in one database, filtered by project and session
Automatic Updates: Changes made in one session are immediately visible to others

Use Cases

Multiple machines: Keep contexts in sync across laptop and desktop
Session continuity: Pick up where you left off after restarting Claude Code

CLI Usage (Alternative)

# Get ChatGPT's opinion on something
./mcp-toolz context save-and-query \
  --type suggestion \
  --title "Redis caching strategy" \
  --content "Use Redis for session storage with 1-hour TTL" \
  --tags "caching,redis"

# Save your current todos
./mcp-toolz todo save \
  --todos '[
    {"content":"Fix auth bug","status":"in_progress","activeForm":"Fixing auth bug"},
    {"content":"Write tests","status":"pending","activeForm":"Writing tests"}
  ]' \
  --context "Working on authentication"

# List everything
./mcp-toolz context list
./mcp-toolz todo list

# Restore todos later
./mcp-toolz todo restore

Command Reference

Context Commands

# Save and query ChatGPT immediately
./mcp-toolz context save-and-query \
  --type <type> \
  --title "Title" \
  --content "..." \
  --tags "tag1,tag2"

# Save without querying
./mcp-toolz context save --type code --file path/to/file.py

# Ask ChatGPT or Claude about existing context
./mcp-toolz context ask-chatgpt <context-id> [--question "Your question"]
./mcp-toolz context ask-claude <context-id> [--question "Your question"]

# Browse and search
./mcp-toolz context list [--limit N] [--type TYPE]
./mcp-toolz context search "query"
./mcp-toolz context show <context-id>

# Delete
./mcp-toolz context delete <context-id>

Context Types:

suggestion - Architecture decisions, implementation plans
code - Code snippets, implementations
conversation - Discussions, Q&A sessions
error - Error messages, stack traces, debugging

Todo Commands

# Save current todos
./mcp-toolz todo save \
  --todos '[{"content":"...","status":"pending","activeForm":"..."}]' \
  --context "What you're working on"

# Restore (defaults to active snapshot for current project)
./mcp-toolz todo restore [<snapshot-id>]

# Browse and search
./mcp-toolz todo list [--project-path PATH]
./mcp-toolz todo search "query"
./mcp-toolz todo show <snapshot-id>

# Delete
./mcp-toolz todo delete <snapshot-id>

Todo Status: pending, in_progress, completed

Get Help

./mcp-toolz --help
./mcp-toolz context --help
./mcp-toolz todo --help

Common Workflows

Get Multiple AI Perspectives

When evaluating an implementation, compare insights from different AI models:

./mcp-toolz context save-and-query \
  --type suggestion \
  --title "Microservices vs Monolith for e-commerce" \
  --content "Building platform with 5 services. Start microservices or monolith first?" \
  --tags "architecture,scalability"

The AI's response appears immediately in your console. You can also ask specific questions or get Claude's perspective:

# Ask a specific question about the context
./mcp-toolz context ask-chatgpt <context-id> --question "What are the scalability concerns?"

# Get Claude's general opinion
./mcp-toolz context ask-claude <context-id>

# Or ask Claude a specific question
./mcp-toolz context ask-claude <context-id> --question "How would you handle database migrations?"

Debug with Two Perspectives

./mcp-toolz context save-and-query \
  --type error \
  --title "CORS issue in production" \
  --content "Error: blocked by CORS policy. Headers: ..." \
  --tags "debugging,cors,production"

Session Continuity

# End of work session
./mcp-toolz todo save \
  --todos '[
    {"content":"Implement login","status":"completed","activeForm":"Implementing login"},
    {"content":"Add OAuth","status":"in_progress","activeForm":"Adding OAuth"},
    {"content":"Write tests","status":"pending","activeForm":"Writing tests"}
  ]' \
  --context "Day 2 of auth feature"

# Next session
./mcp-toolz todo restore

# Session 1: Save interesting discussions
./mcp-toolz context save \
  --type conversation \
  --title "Performance optimization ideas" \
  --content "..." \
  --tags "performance"

# Session 2: Find and review
./mcp-toolz context search "performance"
./mcp-toolz context show <context-id>

# Or ask AI specific questions
./mcp-toolz context ask-chatgpt <context-id> --question "What's the performance impact?"
./mcp-toolz context ask-claude <context-id> --question "Are there any security concerns?"

Environment Variables

# Required (at least one for AI features)
OPENAI_API_KEY=sk-...                              # Your OpenAI API key
ANTHROPIC_API_KEY=sk-ant-...                       # Your Anthropic API key
GOOGLE_API_KEY=...                                 # Your Google API key (for Gemini)
DEEPSEEK_API_KEY=sk-...                            # Your DeepSeek API key

# Optional
MCP_TOOLZ_DB_PATH=~/.mcp-toolz/contexts.db                    # Shared database location (default)
MCP_TOOLZ_MODEL=gpt-5                                         # OpenAI model (default: gpt-5)
MCP_TOOLZ_CLAUDE_MODEL=claude-sonnet-4-5-20250929             # Claude model
MCP_TOOLZ_GEMINI_MODEL=gemini-2.0-flash-thinking-exp-01-21   # Gemini model
MCP_TOOLZ_DEEPSEEK_MODEL=deepseek-chat                        # DeepSeek model

Troubleshooting

"Error 401: Invalid API key"

Verify API keys are set in .env (OPENAI_API_KEY and/or ANTHROPIC_API_KEY)
Check billing is enabled on your OpenAI/Anthropic account
The ./mcp-toolz wrapper automatically unsets shell environment variables to use .env

"No module named context_manager"

Use ./mcp-toolz helper script (recommended)
Or set PYTHONPATH=src before running Python directly

Commands not found

Activate venv: source venv/bin/activate
Make script executable: chmod +x mcp-toolz

Todos not restoring

Check you're in the same project directory
Use ./mcp-toolz todo list to see all snapshots
Restore specific snapshot: ./mcp-toolz todo restore <snapshot-id>

Project Structure

mcp-toolz/
├── src/
│   ├── mcp_server/          # MCP server for Claude Code
│   │   └── server.py        # MCP tools and resources
│   ├── context_manager/     # CLI and storage
│   │   ├── cli.py          # Click-based CLI
│   │   ├── storage.py      # SQLite operations
│   │   ├── openai_client.py # ChatGPT API client
│   │   └── anthropic_client.py # Claude API client
│   └── models.py           # Pydantic data models
├── data/
│   └── contexts.db         # SQLite database
├── requirements.txt
├── requirements-dev.txt
└── mcp-toolz               # Helper script

Development

Setup for Contributors

# Clone and install
git clone https://github.com/taylorleese/mcp-toolz.git
cd mcp-toolz
python3 -m venv venv
source venv/bin/activate
pip install -r requirements-dev.txt

# Install pre-commit hooks (IMPORTANT!)
pre-commit install

# Copy and configure .env
cp .env.example .env
# Edit .env with your API keys

Running Tests

source venv/bin/activate
pytest

Code Quality

# Run all checks (runs automatically on commit after pre-commit install)
pre-commit run --all-files

# Individual tools
black .
ruff check .
mypy src/

Tips

Use descriptive titles - Makes searching easier later
Add relevant tags - Helps organize and find contexts
Be specific in content - More detail = better AI responses
Compare AI opinions - Get both ChatGPT and Claude perspectives on important decisions
Review AI suggestions - They're helpful opinions, not rules
Save todos regularly - Build habit of saving at end of sessions

License

MIT

MCP Toolz

mcp-name: io.github.taylorleese/mcp-toolz

Save contexts and todos across Claude Code sessions, get feedback from ChatGPT, Claude, Gemini, and DeepSeek.

Features

🔌 MCP Server: Works NOW with Claude Code - full tool integration ready
Session Continuity: Never lose context when restarting Claude Code - restore "what was I working on last session"
Project Organization: Contexts and todos automatically organized by project directory
Session Tracking: Every Claude Code session gets a unique ID - track your work over time
AI Feedback: Get feedback from ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), and DeepSeek on your code and decisions
Context Types: Save conversations, code snippets, architectural suggestions, or error traces
Persistent Todos: Save and restore your todo list across sessions - never forget where you left off
Full-Text Search: Find anything by content, tags, project, or session
CLI + MCP: Use via Claude Code MCP tools or standalone CLI commands

Quick Start

Installation

From PyPI (Recommended)

pip install mcp-toolz

From Source (Development)

# Clone the repository
git clone https://github.com/taylorleese/mcp-toolz.git
cd mcp-toolz

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # macOS/Linux
# or: venv\Scripts\activate  # Windows

# Install in editable mode with dev dependencies
pip install -e ".[dev]"

Configuration

# Set your API keys as environment variables (at least one required for AI features)
export OPENAI_API_KEY=sk-...           # For ChatGPT
export ANTHROPIC_API_KEY=sk-ant-...    # For Claude
export GOOGLE_API_KEY=...              # For Gemini
export DEEPSEEK_API_KEY=sk-...         # For DeepSeek

# Or create a .env file (if installing from source)
cp .env.example .env
# Edit .env and add your API keys

MCP Server Setup (Recommended)

The primary way to use mcp-toolz is via the MCP server in Claude Code:

Add to Claude Code settings (add this JSON to your Claude Code MCP settings):

If installed via pip:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GOOGLE_API_KEY": "...",
        "DEEPSEEK_API_KEY": "sk-..."
      }
    }
  }
}

If installed from source:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/absolute/path/to/mcp-toolz",
      "env": {
        "PYTHONPATH": "/absolute/path/to/mcp-toolz/src"
      }
    }
  }
}

Configure API keys - Add your API keys to the env section (pip) or .env file (source)
Restart Claude Code to load the MCP server
Use MCP tools in Claude Code:
- "Save this context about authentication"
- "Ask ChatGPT about the last context I saved"
- "Show my active todos"
- "Search contexts tagged with 'bug'"

All MCP tools are automatically available - see MCP Server Tools below.

MCP Server Tools

The MCP server works NOW with Claude Code and provides these tools:

Context Tools:

context_save - Save a new context (automatically includes session info)
context_search - Search by query or tags
context_get - Get by ID
context_list - List recent
context_delete - Delete by ID

AI Feedback Tools:

ask_chatgpt - Get ChatGPT's analysis of a context (supports custom questions)
ask_claude - Get Claude's analysis of a context (supports custom questions)
ask_gemini - Get Gemini's analysis of a context (supports custom questions)
ask_deepseek - Get DeepSeek's analysis of a context (supports custom questions)

Todo Tools:

todo_search - Search snapshots
todo_get - Get by ID
todo_list - List recent
todo_save - Save snapshot
todo_restore - Get active/specific snapshot
todo_delete - Delete by ID

Session Tracking: When saving contexts through MCP tools, they are automatically tagged with:

Current project directory (project_path)
Session ID (unique per Claude Code session)
Session timestamp (when the session started)

Future: Once ChatGPT Desktop adds MCP support, you'll be able to use these same tools there too.

Usage Examples

Here are practical examples of how to use mcp-toolz in Claude Code:

Example 1: Get Multiple AI Perspectives on Architecture Decisions

Prompt:

I'm deciding between using Redis or Memcached for caching user sessions.
Save this as a context and ask ChatGPT for their analysis.
Use tags: caching, redis, memcached, architecture

What happens:

Claude Code uses context_save to save your architectural decision
Then uses ask_chatgpt to get ChatGPT's perspective
You can compare multiple AI perspectives to inform your decision

Follow-up prompts:

"Ask Claude the same question for comparison"
"Ask Gemini for another perspective"
"What does DeepSeek think about this?"
"Search my contexts tagged with 'architecture'"

Example 2: Session Continuity - Never Lose Your Place

Prompt (end of work session):

Save my current todo list so I can restore it tomorrow

What happens:

Claude Code uses todo_save to snapshot your current work state
Todos are saved with project path and timestamp

Next day prompt:

What was I working on yesterday? Restore my todos.

What happens:

Claude Code uses todo_restore to get your last snapshot
Shows you exactly where you left off
You can jump right back into work

Example 3: Debug with Multiple AI Perspectives

Prompt:

I'm getting "TypeError: Cannot read property 'map' of undefined" in my React component.
The error occurs in UserList.jsx when rendering the users array.
Save this as an error context and ask ChatGPT, Claude, and Gemini for debugging suggestions.
Tags: react, debugging, javascript

What happens:

Claude Code uses context_save to record the error
Uses ask_chatgpt to get OpenAI's debugging approach
Uses ask_claude to get Anthropic's perspective
Uses ask_gemini for Google's analysis
You can compare different debugging strategies from multiple AI models

Follow-up prompts:

"Search for other contexts tagged with 'react' bugs"
"Show me contexts from my last session"

Example 4: Track Performance Optimization Ideas

Prompt:

Save this performance optimization idea: "Lazy load images below the fold using
Intersection Observer API. Estimated 40% reduction in initial page load."
Type: suggestion, Tags: performance, optimization, images

What happens:

Claude Code uses context_save with type "suggestion"
Context is searchable and tied to current project
Available across all future sessions

Later prompt:

Search my contexts for performance optimization ideas

What happens:

Claude Code uses context_search with your query
Returns all matching contexts across sessions
You can review past optimization ideas

Prompt (in Project A):

I figured out how to handle OAuth refresh tokens properly.
Save this so I can reference it in other projects:
"Store refresh tokens in httpOnly cookies, access tokens in memory only.
Rotate refresh tokens on each use. Set 7-day expiry on refresh, 15min on access."
Type: code, Tags: oauth, security, authentication

Prompt (later in Project B):

How did I implement OAuth refresh tokens in my last project?
Search for contexts about oauth and show me what I saved.

What happens:

Claude Code uses context_search to find your OAuth implementation
Retrieves the context across projects
You reuse your own knowledge without starting from scratch

mcp-toolz makes it easy to share contexts and todos across multiple Claude Code sessions or agents.

MCP Resources (Passive Discovery)

Claude Code can automatically discover and read contexts/todos via MCP resources:

Context Resources:

mcp-toolz://contexts/project/recent - Recent contexts for current project
mcp-toolz://contexts/project/sessions - List of recent Claude Code sessions for current project
mcp-toolz://contexts/session/{session_id} - All contexts from a specific session

Todo Resources:

mcp-toolz://todos/recent - Last 20 todo snapshots (all projects)
mcp-toolz://todos/active - Active todos for current working directory

Session Tracking: Each Claude Code session automatically gets a unique session ID. All contexts saved during that session are tagged with:

session_id - UUID of the Claude Code session
session_timestamp - When the session started
project_path - Directory where the context was created

This makes it easy to restore context from previous sessions: "Show me what I was working on in my last session"

Resources are read-only views into the shared database. Claude Code can discover them automatically without explicit tool calls.

Shared Database Setup

By default, mcp-toolz stores all data in ~/.mcp-toolz/contexts.db, which is automatically shared across all projects on the same machine. No additional configuration needed!

For advanced use cases (syncing across multiple machines via Dropbox, iCloud, etc.):

Choose a synced location for the database:

# Example: Use a synced folder (Dropbox, iCloud, network drive)
mkdir -p ~/Dropbox/mcp-toolz-shared

Update .env file or MCP config to point to the synced database:

# In .env file
MCP_TOOLZ_DB_PATH=~/Dropbox/mcp-toolz-shared/contexts.db

Or in your MCP config:

{
  "mcpServers": {
    "mcp-toolz": {
      "command": "python",
      "args": ["-m", "mcp_server"],
      "cwd": "/absolute/path/to/mcp-toolz",
      "env": {
        "PYTHONPATH": "/absolute/path/to/mcp-toolz/src",
        "MCP_TOOLZ_DB_PATH": "/Users/you/Dropbox/mcp-toolz-shared/contexts.db"
      }
    }
  }
}

Restart Claude Code - it now uses the synced database location

How It Works

Contexts: Organized by project_path (each directory gets its own contexts)
Session Tracking: Contexts tagged with session ID and timestamp for easy restoration
Todos: Organized by project_path (each directory gets its own snapshots)
Single SQLite DB: All data stored in one database, filtered by project and session
Automatic Updates: Changes made in one session are immediately visible to others

Use Cases

Multiple machines: Keep contexts in sync across laptop and desktop
Session continuity: Pick up where you left off after restarting Claude Code

CLI Usage (Alternative)

# Get ChatGPT's opinion on something
./mcp-toolz context save-and-query \
  --type suggestion \
  --title "Redis caching strategy" \
  --content "Use Redis for session storage with 1-hour TTL" \
  --tags "caching,redis"

# Save your current todos
./mcp-toolz todo save \
  --todos '[
    {"content":"Fix auth bug","status":"in_progress","activeForm":"Fixing auth bug"},
    {"content":"Write tests","status":"pending","activeForm":"Writing tests"}
  ]' \
  --context "Working on authentication"

# List everything
./mcp-toolz context list
./mcp-toolz todo list

# Restore todos later
./mcp-toolz todo restore

Command Reference

Context Commands

# Save and query ChatGPT immediately
./mcp-toolz context save-and-query \
  --type <type> \
  --title "Title" \
  --content "..." \
  --tags "tag1,tag2"

# Save without querying
./mcp-toolz context save --type code --file path/to/file.py

# Ask ChatGPT or Claude about existing context
./mcp-toolz context ask-chatgpt <context-id> [--question "Your question"]
./mcp-toolz context ask-claude <context-id> [--question "Your question"]

# Browse and search
./mcp-toolz context list [--limit N] [--type TYPE]
./mcp-toolz context search "query"
./mcp-toolz context show <context-id>

# Delete
./mcp-toolz context delete <context-id>

Context Types:

suggestion - Architecture decisions, implementation plans
code - Code snippets, implementations
conversation - Discussions, Q&A sessions
error - Error messages, stack traces, debugging

Todo Commands

# Save current todos
./mcp-toolz todo save \
  --todos '[{"content":"...","status":"pending","activeForm":"..."}]' \
  --context "What you're working on"

# Restore (defaults to active snapshot for current project)
./mcp-toolz todo restore [<snapshot-id>]

# Browse and search
./mcp-toolz todo list [--project-path PATH]
./mcp-toolz todo search "query"
./mcp-toolz todo show <snapshot-id>

# Delete
./mcp-toolz todo delete <snapshot-id>

Todo Status: pending, in_progress, completed

Get Help

./mcp-toolz --help
./mcp-toolz context --help
./mcp-toolz todo --help

Common Workflows

Get Multiple AI Perspectives

When evaluating an implementation, compare insights from different AI models:

./mcp-toolz context save-and-query \
  --type suggestion \
  --title "Microservices vs Monolith for e-commerce" \
  --content "Building platform with 5 services. Start microservices or monolith first?" \
  --tags "architecture,scalability"

The AI's response appears immediately in your console. You can also ask specific questions or get Claude's perspective:

# Ask a specific question about the context
./mcp-toolz context ask-chatgpt <context-id> --question "What are the scalability concerns?"

# Get Claude's general opinion
./mcp-toolz context ask-claude <context-id>

# Or ask Claude a specific question
./mcp-toolz context ask-claude <context-id> --question "How would you handle database migrations?"

Debug with Two Perspectives

./mcp-toolz context save-and-query \
  --type error \
  --title "CORS issue in production" \
  --content "Error: blocked by CORS policy. Headers: ..." \
  --tags "debugging,cors,production"

Session Continuity

# End of work session
./mcp-toolz todo save \
  --todos '[
    {"content":"Implement login","status":"completed","activeForm":"Implementing login"},
    {"content":"Add OAuth","status":"in_progress","activeForm":"Adding OAuth"},
    {"content":"Write tests","status":"pending","activeForm":"Writing tests"}
  ]' \
  --context "Day 2 of auth feature"

# Next session
./mcp-toolz todo restore

# Session 1: Save interesting discussions
./mcp-toolz context save \
  --type conversation \
  --title "Performance optimization ideas" \
  --content "..." \
  --tags "performance"

# Session 2: Find and review
./mcp-toolz context search "performance"
./mcp-toolz context show <context-id>

# Or ask AI specific questions
./mcp-toolz context ask-chatgpt <context-id> --question "What's the performance impact?"
./mcp-toolz context ask-claude <context-id> --question "Are there any security concerns?"

Environment Variables

# Required (at least one for AI features)
OPENAI_API_KEY=sk-...                              # Your OpenAI API key
ANTHROPIC_API_KEY=sk-ant-...                       # Your Anthropic API key
GOOGLE_API_KEY=...                                 # Your Google API key (for Gemini)
DEEPSEEK_API_KEY=sk-...                            # Your DeepSeek API key

# Optional
MCP_TOOLZ_DB_PATH=~/.mcp-toolz/contexts.db                    # Shared database location (default)
MCP_TOOLZ_MODEL=gpt-5                                         # OpenAI model (default: gpt-5)
MCP_TOOLZ_CLAUDE_MODEL=claude-sonnet-4-5-20250929             # Claude model
MCP_TOOLZ_GEMINI_MODEL=gemini-2.0-flash-thinking-exp-01-21   # Gemini model
MCP_TOOLZ_DEEPSEEK_MODEL=deepseek-chat                        # DeepSeek model

Troubleshooting

"Error 401: Invalid API key"

Verify API keys are set in .env (OPENAI_API_KEY and/or ANTHROPIC_API_KEY)
Check billing is enabled on your OpenAI/Anthropic account
The ./mcp-toolz wrapper automatically unsets shell environment variables to use .env

"No module named context_manager"

Use ./mcp-toolz helper script (recommended)
Or set PYTHONPATH=src before running Python directly

Commands not found

Activate venv: source venv/bin/activate
Make script executable: chmod +x mcp-toolz

Todos not restoring

Check you're in the same project directory
Use ./mcp-toolz todo list to see all snapshots
Restore specific snapshot: ./mcp-toolz todo restore <snapshot-id>

Project Structure

mcp-toolz/
├── src/
│   ├── mcp_server/          # MCP server for Claude Code
│   │   └── server.py        # MCP tools and resources
│   ├── context_manager/     # CLI and storage
│   │   ├── cli.py          # Click-based CLI
│   │   ├── storage.py      # SQLite operations
│   │   ├── openai_client.py # ChatGPT API client
│   │   └── anthropic_client.py # Claude API client
│   └── models.py           # Pydantic data models
├── data/
│   └── contexts.db         # SQLite database
├── requirements.txt
├── requirements-dev.txt
└── mcp-toolz               # Helper script

Development

Setup for Contributors

# Clone and install
git clone https://github.com/taylorleese/mcp-toolz.git
cd mcp-toolz
python3 -m venv venv
source venv/bin/activate
pip install -r requirements-dev.txt

# Install pre-commit hooks (IMPORTANT!)
pre-commit install

# Copy and configure .env
cp .env.example .env
# Edit .env with your API keys

Running Tests

source venv/bin/activate
pytest

Code Quality

# Run all checks (runs automatically on commit after pre-commit install)
pre-commit run --all-files

# Individual tools
black .
ruff check .
mypy src/

Tips

Use descriptive titles - Makes searching easier later
Add relevant tags - Helps organize and find contexts
Be specific in content - More detail = better AI responses
Compare AI opinions - Get both ChatGPT and Claude perspectives on important decisions
Review AI suggestions - They're helpful opinions, not rules
Save todos regularly - Build habit of saving at end of sessions

License

MIT

MCP Tools

MCP Toolz

Features

Quick Start

Installation

From PyPI (Recommended)

From Source (Development)

Configuration

MCP Server Setup (Recommended)

MCP Server Tools

Usage Examples

Example 1: Get Multiple AI Perspectives on Architecture Decisions

Example 2: Session Continuity - Never Lose Your Place

Example 3: Debug with Multiple AI Perspectives

Example 4: Track Performance Optimization Ideas

Example 5: Cross-Session Knowledge Sharing

Sharing Contexts Between Agents

MCP Resources (Passive Discovery)

Shared Database Setup

How It Works

Use Cases

CLI Usage (Alternative)

Command Reference

Context Commands

Todo Commands

Get Help

Common Workflows

Get Multiple AI Perspectives

Debug with Two Perspectives

Session Continuity

Share Across Claude Code Sessions

Environment Variables

Troubleshooting

"Error 401: Invalid API key"

"No module named context_manager"

Commands not found

Todos not restoring

Project Structure

Development

Setup for Contributors

Running Tests

Code Quality

Tips

License

MCP Toolz

Features

Quick Start

Installation

From PyPI (Recommended)

From Source (Development)

Configuration

MCP Server Setup (Recommended)

MCP Server Tools

Usage Examples

Example 1: Get Multiple AI Perspectives on Architecture Decisions

Example 2: Session Continuity - Never Lose Your Place

Example 3: Debug with Multiple AI Perspectives

Example 4: Track Performance Optimization Ideas

Example 5: Cross-Session Knowledge Sharing

Sharing Contexts Between Agents

MCP Resources (Passive Discovery)

Shared Database Setup

How It Works

Use Cases

CLI Usage (Alternative)

Command Reference

Context Commands

Todo Commands

Get Help

Common Workflows

Get Multiple AI Perspectives

Debug with Two Perspectives

Session Continuity

Share Across Claude Code Sessions

Environment Variables

Troubleshooting

"Error 401: Invalid API key"

"No module named context_manager"

Commands not found

Todos not restoring